SoroushVahidi/scidocs huggingface dataset:
Evaluative Language Beyond Bags of Words: Linguistic Insights and Computational Applications
(2017)

The study of evaluation, affect, and subjectivity is a multidisciplinary enterprise, including sociology, psychology, economics, linguistics, and computer science. A number of excellent computational linguistics and linguistic surveys of the field exist. Most surveys, however, do not bring the two disciplines together to show how methods from linguistics can benefit computational sentiment analysis systems. In this survey, we show how incorporating linguistic insights, discourse information, and other contextual phenomena, in combination with the statistical exploitation of data, can result in an improvement over approaches that take advantage of only one of these perspectives. We first provide a comprehensive introduction to evaluative language from both a linguistic and computational perspective. We then argue that the standard computational definition of the concept of evaluative language neglects the dynamic nature of evaluation, in which the interpretation of a given evaluation depends on linguistic and extra-linguistic contextual factors. We thus propose a dynamic definition that incorporates update functions. The update functions allow for different contextual aspects to be incorporated into the calculation of sentiment for evaluative words or expressions, and can be applied at all levels of discourse. We explore each level and highlight which linguistic aspects contribute to accurate extraction of sentiment. We end the review by outlining what we believe the future directions of sentiment analysis are, and the role that discourse and contextual information need to play.
Performing a check-in: emerging practices, norms and 'conflicts' in location-sharing using foursquare
(2011)

Location-sharing services have a long history in research, but have only recently become available for consumers. Most popular commercial location-sharing services differ from previous research efforts in important ways: they use manual 'check-ins' to pair user location with semantically named venues rather than tracking; venues are visible to all users; location is shared with a potentially very large audience; and they employ incentives. By analysis of 20 in-depth interviews with foursquare users and 47 survey responses, we gained insight into emerging social practices surrounding location-sharing. We see a shift from privacy issues and data deluge, to more performative considerations in sharing one's location. We discuss performance aspects enabled by check-ins to public venues, and show emergent, but sometimes conflicting norms (not) to check-in.
Predicting Airfare Prices
(2012)

Airlines implement dynamic pricing for their tickets, and base their pricing decisions on demand estimation models. The reason for such a complicated system is that each flight only has a set number of seats to sell, so airlines have to regulate demand. In the case where demand is expected to exceed capacity, the airline may increase prices, to decrease the rate at which seats fill. On the other hand, a seat that goes unsold represents a loss of revenue, and selling that seat for any price above the service cost for a single passenger would have been a more preferable scenario.
Functional imaging of human crossmodal identification and object recognition
(2005)

The perception of objects is a cognitive function of prime importance. In everyday life, object perception benefits from the coordinated interplay of vision, audition, and touch. The different sensory modalities provide both complementary and redundant information about objects, which may improve recognition speed and accuracy in many circumstances. We review crossmodal studies of object recognition in humans that mainly employed functional magnetic resonance imaging (fMRI). These studies show that visual, tactile, and auditory information about objects can activate cortical association areas that were once believed to be modality-specific. Processing converges either in multisensory zones or via direct crossmodal interaction of modality-specific cortices without relay through multisensory regions. We integrate these findings with existing theories about semantic processing and propose a general mechanism for crossmodal object recognition: The recruitment and location of multisensory convergence zones varies depending on the information content and the dominant modality.
A gene module associated with dysregulated TCR signaling pathways in CD4+ T cell subsets in rheumatoid arthritis.
(2018)

We analyzed the transcriptome of detailed CD4+ T cell subsets including them after abatacept treatment, and examined the difference among CD4+ T cell subsets and identified gene sets that are closely associated disease activity and abatacept treatment. Seven CD4+ T cell subsets (naive, Th1, Th17, Th1/17, nonTh1/17, Tfh and Treg) were sorted from PBMCs taken from 10 RA patients and 10 healthy controls, and three RA patients donated samples before and 6 months after abatacept treatment. Paired-end RNA sequencing was performed using HiSeq 2500. A total of 149 samples except for 12 outliers were analyzed. Overview of expression pattern of RA revealed that administration of abatacept exerts a large shift toward the expression pattern of HC. Most of differentially expressed gene (DEG) upregulated in RA (n = 1776) were downregulated with abatacept treatment (n = 1349). Inversely, most of DEG downregulated in RA (n = 1860) were upregulated with abatacept treatment (n = 1294). This DEG-based analysis revealed shared pathway changes in RA CD4+ T cell subsets. Knowledge-based pathway analysis revealed the upregulation of activation-related pathways in RA that was substantially ameliorated by abatacept. Weighted gene co-expression network analysis (WGCNA) evaluated CD4+ T cells collectively and identified a gene module that consisted of 227 genes and was correlated with DAS28-CRP (Spearman's rho = 0.46, p = 4 × 10-9) and abatacept administration (Spearman's rho = -0.91, p = 5 × 10-57). The most highly connected 30 genes of this module included ZAP70 and JAK3, and pathway analysis of this module revealed dysregulation of the TCR signaling pathway network, which was ameliorated by abatacept.
Dynamic Social Network Analysis of a Dark Network: Identifying Significant Facilitators
(2007)

"Dark Networks" refer to various illegal and covert social networks like criminal and terrorist networks. These networks evolve over time with the formation and dissolution of links to survive control efforts by authorities. Previous studies have shown that the link formation process in such networks is influenced by a set of facilitators. However, there have been few empirical evaluations to determine the significant facilitators. In this study, we used dynamic social network analysis methods to examine several plausible link formation facilitators in a large-scale real-world narcotics network. Multivariate Cox regression showed that mutual acquaintance and vehicle affiliations were significant facilitators in the network under study. These findings provide insights into the link formation processes and the resilience of dark networks. They also can be used to help authorities predict co-offending in future crimes.
Margin based feature selection - theory and algorithms
(2004)

Feature selection is the task of choosing a small set out of a given set of features that capture the relevant properties of the data. In the context of supervised classification problems the relevance is determined by the given labels on the training data. A good choice of features is a key for building compact and accurate classifiers. In this paper we introduce a margin based feature selection criterion and apply it to measure the quality of sets of features. Using margins we devise novel selection algorithms for multi-class classification problems and provide theoretical generalization bound. We also study the well known Relief algorithm and show that it resembles a gradient ascent over our margin criterion. We apply our new algorithm to various datasets and show that our new Simba algorithm, which directly optimizes the margin, outperforms Relief.
Time-Elastic Generative Model for Acceleration Time Series in Human Activity Recognition
(2017)

Body-worn sensors in general and accelerometers in particular have been widely used in order to detect human movements and activities. The execution of each type of movement by each particular individual generates sequences of time series of sensed data from which specific movement related patterns can be assessed. Several machine learning algorithms have been used over windowed segments of sensed data in order to detect such patterns in activity recognition based on intermediate features (either hand-crafted or automatically learned from data). The underlying assumption is that the computed features will capture statistical differences that can properly classify different movements and activities after a training phase based on sensed data. In order to achieve high accuracy and recall rates (and guarantee the generalization of the system to new users), the training data have to contain enough information to characterize all possible ways of executing the activity or movement to be detected. This could imply large amounts of data and a complex and time-consuming training phase, which has been shown to be even more relevant when automatically learning the optimal features to be used. In this paper, we present a novel generative model that is able to generate sequences of time series for characterizing a particular movement based on the time elasticity properties of the sensed data. The model is used to train a stack of auto-encoders in order to learn the particular features able to detect human movements. The results of movement detection using a newly generated database with information on five users performing six different movements are presented. The generalization of results using an existing database is also presented in the paper. The results show that the proposed mechanism is able to obtain acceptable recognition rates (F = 0.77) even in the case of using different people executing a different sequence of movements and using different hardware.
Effective Clipart Image Vectorization through Direct Optimization of Bezigons
(2016)

Bezigons, i.e., closed paths composed of Bézier curves, have been widely employed to describe shapes in image vectorization results. However, most existing vectorization techniques infer the bezigons by simply approximating an intermediate vector representation (such as polygons). Consequently, the resultant bezigons are sometimes imperfect due to accumulated errors, fitting ambiguities, and a lack of curve priors, especially for low-resolution images. In this paper, we describe a novel method for vectorizing clipart images. In contrast to previous methods, we directly optimize the bezigons rather than using other intermediate representations; therefore, the resultant bezigons are not only of higher fidelity compared with the original raster image but also more reasonable because they were traced by a proficient expert. To enable such optimization, we have overcome several challenges and have devised a differentiable data energy as well as several curve-based prior terms. To improve the efficiency of the optimization, we also take advantage of the local control property of bezigons and adopt an overlapped piecewise optimization strategy. The experimental results show that our method outperforms both the current state-of-the-art method and commonly used commercial software in terms of bezigon quality.
i-JEN: Visual Interactive Malaysia Crime News Retrieval System
(2011)

Supporting crime news investigation involves a mechanism to help monitor the current and past status of criminal events. We believe this could be well facilitated by focusing on the user interfaces and the event crime model aspects. In this paper we discuss on a development of Visual Interactive Malaysia Crime News Retrieval System (i-JEN) and describe the approach, user studies and planned, the system architecture and future plan. Our main objectives are to construct crime-based event; investigate the use of crimebased event in improving the classification and clustering; develop an interactive crime news retrieval system; visualize crime news in an effective and interactive way; integrate them into a usable and robust system and evaluate the usability and system performance. The system will serve as a news monitoring system which aims to automatically organize, retrieve and present the crime news in such a way as to support an effective monitoring, searching, and browsing for the target users groups of general public, news analysts and policemen or crime investigators. The study will contribute to the better understanding of the crime data consumption in the Malaysian context as well as the developed system with the visualisation features to address crime data and the eventual goal of combating the crimes.
Knowledge management, innovation and firm performance
(2005)

Purpose – To provide important empirical evidence to support the role of knowledge management within firms. Design/methodology/approach – Data were collected using a mail survey sent to CEOs representing firms with 50 or more employees from a cross-section of industries. A total of 1,743 surveys were mailed out and 443 were returned and usable (27.8 percent response rate). The sample was checked for response and non-response bias. Hypotheses were tested using structural equation modelling. Findings – This paper presents knowledge management as a coordinating mechanism. Empirical evidence supports the view that a firm with a knowledge management capability will use resources more efficiently and so will be more innovative and perform better. Research limitations/implications – The sample slightly over-represented larger firms. Data were also collected in New Zealand. As with most studies, it is important to replicate this study in different contexts. Practical implications – Knowledge management is embraced in many organizations and requires a business case to justify expenditure on programs to implement knowledge management behaviours and practices or hardware and software solutions. This paper provides support for the importance of knowledge management to enhance innovation and performance. Originality/value – This paper is one of the first to find empirical support for the role of knowledge management within firms. Further, the positioning of knowledge management as a coordinating mechanism is also an important contribution to our thinking on this topic.
Not All Oil Price Shocks Are Alike : Disentangling Demand and Supply Shocks in the Crude Oil Market
(2006)

Using a newly developed measure of global real economic activity, a structural decomposition of the real price of crude oil into three components is proposed: crude oil supply shocks; shocks to the global demand for all industrial commodities; and demand shocks that are specific to the crude oil market. The latter shock is designed to capture shifts in the price of oil driven by higher precautionary demand associated with concerns about future oil supply shortfalls. The paper estimates the dynamic effects of these shocks on the real price of oil. A historical decomposition sheds light on the causes of the major oil price shocks since 1975. The implications of higher oil prices for U.S. real GDP and CPI inflation are shown to depend on the cause of the oil price increase. Changes in the composition of shocks help explain why regressions of macroeconomic aggregates on oil prices tend to be unstable. Evidence that the recent increase in crude oil prices was driven primarily by global aggregate demand shocks helps explain why this oil price shock so far has failed to cause a major recession in the U.S.
OntoDM: An Ontology of Data Mining
(2008)

Motivated by the need for unification of the field of data mining and the growing demand for formalized representation of outcomes of research, we address the task of constructing an ontology of data mining. The proposed ontology, named OntoDM, is based on a recent proposal of a general framework for data mining, and includes definitions of basic data mining entities, such as datatype and dataset, data mining task, data mining algorithm and components thereof (e.g., distance function), etc. It also allows for the definition of more complex entities, e.g., constraints in constraint-based data mining, sets of such constraints (inductive queries) and data mining scenarios (sequences of inductive queries). Unlike most existing approaches to constructing ontologies of data mining, OntoDM is a deep/heavy-weight ontology and follows best practices in ontology engineering, such as not allowing multiple inheritance of classes, using a predefined set of relations and using a top level ontology.
Improving RDF Data Through Association Rule Mining
(2013)

Linked Open Data comprises very many and often large public data sets, which are mostly presented in the Rdf triple structure of subject, predicate, and object. However, the heterogeneity of available open data requires significant integration steps before it can be used in applications. A promising and novel technique to explore such data is the use of association rule mining. We introduce “mining configurations”, which allow us to mine Rdf data sets in various ways. Different configurations enable us to identify schema and value dependencies that in combination result in interesting use cases. We present rule-based approaches for predicate suggestion, data enrichment, ontology improvement, and query relaxation. On the one hand we prevent inconsistencies in the data through predicate suggestion, enrichment with missing facts, and alignment of the corresponding ontology. On the other hand we support users to handle inconsistencies during query formulation through predicate expansion techniques. Based on these approaches, we show that association rule mining benefits the integration and usability of Rdf data.
PathSim: Meta Path-Based Top-K Similarity Search in Heterogeneous Information Networks
(2011)

Similarity search is a primitive operation in database and Web search engines. With the advent of large-scale heterogeneous information networks that consist of multi-typed, interconnected objects, such as the bibliographic networks and social media networks, it is important to study similarity search in such networks. Intuitively, two objects are similar if they are linked by many paths in the network. However, most existing similarity measures are defined for homogeneous networks. Different semantic meanings behind paths are not taken into consideration. Thus they cannot be directly applied to heterogeneous networks. In this paper, we study similarity search that is defined among the same type of objects in heterogeneous networks. Moreover, by considering different linkage paths in a network, one could derive various similarity semantics. Therefore, we introduce the concept of meta path-based similarity, where a meta path is a path consisting of a sequence of relations defined between different object types (i.e., structural paths at the meta level). No matter whether a user would like to explicitly specify a path combination given sufficient domain knowledge, or choose the best path by experimental trials, or simply provide training examples to learn it, meta path forms a common base for a network-based similarity search engine. In particular, under the meta path framework we define a novel similarity measure called PathSim that is able to find peer objects in the network (e.g., find authors in the similar field and with similar reputation), which turns out to be more meaningful in many scenarios compared with random-walk based similarity measures. In order to support fast online query processing for PathSim queries, we develop an efficient solution that partially materializes short meta paths and then concatenates them online to compute top-k results. Experiments on real data sets demonstrate the effectiveness and efficiency of our proposed paradigm.
Evaluating question answering over linked data
(2013)

The availability of large amounts of open, distributed and structured semantic data on the web has no precedent in the history of computer science. In recent years, there have been important advances in semantic search and question answering over RDF data. In particular, natural language interfaces to online semantic data have the advantage that they can exploit the expressive power of Semantic Web data models and query languages, while at the same time hiding their complexity from the user. However, despite the increasing interest in this area, there are no evaluations so far that systematically evaluate this kind of systems, in contrast to traditional question answering and search interfaces to document spaces. To address this gap, we have set up a series of evaluation challenges for question answering over linked data. The main goal of the challenge was to get insight into the strengths, capabilities and current shortcomings of question answering systems as interfaces to query linked data sources, as well as benchmarking how these interaction paradigms can deal with the fact that the amount of RDF data available on the web is very large and heterogeneous with respect to the vocabularies and schemas used. Here we report on the results from the first and second of such evaluation campaigns. We also discuss how the second evaluation addressed some of the issues and limitations which arose from the first one, as well as the open issues to be addressed in future competitions.
dbrec - Music Recommendations Using DBpedia
(2010)

This paper describes the theoretical background and the implementation of dbrec, a music recommendation system built on top of DBpedia, offering recommendations for more than 39,000 bands and solo artists. We discuss the various challenges and lessons learnt while building it, providing relevant insights for people developing applications consuming Linked Data. Furthermore, we provide a user-centric evaluation of the system, notably by comparing it to last.fm.
Query modeling for entity search based on terms, categories, and examples
(2011)

Users often search for entities instead of documents, and in this setting, are willing to provide extra input, in addition to a series of query terms, such as category information and example entities. We propose a general probabilistic framework for entity search to evaluate and provide insights in the many ways of using these types of input for query modeling. We focus on the use of category information and show the advantage of a category-based representation over a term-based representation, and also demonstrate the effectiveness of category-based expansion using example entities. Our best performing model shows very competitive performance on the INEX-XER entity ranking and list completion tasks.